Adaptive dynamic planning control method and system for energy storage station, and storage medium

ABSTRACT

An adaptive dynamic planning control method and system for a large-scale energy storage station. The method comprises: setting a structure and control target parameters of an adaptive dynamic planning control system; initializing the parameters and importing an initial state of a controlled object; calculating an original wind electricity power fluctuation rate at a current moment t and smoothing the original wind electricity power according to a change rate control strategy; calculating a smoothed wind storage power fluctuation rate, a power of an energy storage system, and a state of charge (SOC) of the energy storage system; initializing and training an evaluation module and an execution module; calculating and saving a control strategy, a smoothed wind storage power fluctuation rate, an energy storage power and a (SOC) at each moment; and outputting the control strategy at each moment, the smoothed wind storage power fluctuation rate, the energy storage power and the (SOC).

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is the U.S. National Stage Application ofInternational Patent Application No. PCT/CN2017/082564, filed on Apr.28, 2017, which claims benefit of Chinese Patent Application No.201610278732.6, filed on Apr. 28, 2016. The contents of each of theseapplications the application are hereby incorporated by reference intheir its entirety.

The present application is based on, and claims priority to, ChinesePatent Application No. 201610278732.6, filed on Apr. 28, 2016, thecontents of which are hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to a smart grid, an Internet of energysources, and energy storage, and in particular to a method, system, andstorage medium for Adaptive Dynamic Programming (ADP) control by a powerstation for large-scale energy storage.

BACKGROUND

With constant development of wind power generation and photovoltaicpower generation, as well as large-scale incorporation of new energypower generation (such as the wind power generation, the photovoltaicpower generation, etc.) into a grid, a growing concern is fluctuation(or turbulence) of power output thereof. With large-scale incorporationof the new energy power generation, such as the wind power generation,the photovoltaic power generation, etc., into the grid, volatility andintermittence associated therewith may impact safety and stability ofgrid operation, quality of electric power, etc. Therefore, incorporationof the wind power generation and the photovoltaic power generation isquite limited in actual application at present, which is against thedevelopment of the new energy power generation, such as the wind powergeneration, the photovoltaic power generation, etc. It is vital tocontrol the turbulence of the power output of the new energy powergeneration for safe, stable, economical operation of the grid. Impact ofthe turbulence of the power output of the new energy power generation onthe grid may be effectively suppressed by charging and discharging asystem for energy storage, thereby lowering volatility caused by asystem for new energy power generation, improving capacity of the gridin accommodating the new energy power generation.

There may be different forms of energy storage, such as physical energystorage, electrochemical energy storage, electromagnetic energy storage,etc. Among them, energy storage by battery is experiencing a fastgrowth, with a power station for energy storage of up to one MegaWatt(MW) or even tens of MWs. Therefore, power generated using the newenergy may be smoothed by equipping a system for large-scale energystorage by battery of certain capacity, and optimizing charging anddischarging of the system for energy storage by battery according to thepower output of the new energy power generation. In addition, overalloptimization may be performed according to an index such as a rate ofturbulence of the power output of the new energy power generation, aState Of Charge (SOC) of the system for energy storage, etc., to meet ademand for incorporating the new energy power generation, such as thewind power generation, the photovoltaic power generation, etc., into thegrid.

Up to now, multiple bases for the new energy power generation of tens ofmillions of KiloWatts (KW) have been built in China. A grid in an areareach in the new energy power generation may generally demand energystorage by battery of capacity of at least tens of MWs or even hundredsof MWs. It is vital to include a power station for energy storage bybattery of hundreds of MWs in cluster control as well as systemscheduling and operation for the new energy power generation in order tobreak through a bottleneck in delivery and absorption of the new energypower generation. Large-scale energy storage is a key technology forsupporting use of renewable energy power generation in China. Toincorporate the renewable energy power generation into the grid on alarge scale, randomness thereof may be reduced and adjustability thereofmay be improved by combining the energy storage with the renewableenergy power generation. Adaptability of the grid to the renewableenergy power generation may be improved by applying grid-level energystorage. At present, energy storage, as a schedulable resource of thegrid, is of great value and widely applicable.

In grid-level application, energy storage may have to support power onmultiple time scales, such as from seconds to hours. To incorporate boththe energy storage and the new energy power generation into the grid.Overall, a power station for energy storage by battery of hundreds ofMWs may have to respond to the new energy power generation on differenttime scales, such as from seconds to minutes. There is a pressing needfor implementing multi-objective coordinated optimization of overallpower output of a power station for energy storage by battery ofhundreds of MWs as needed, such as according to the turbulence of thepower output of large-scale new energy power generation, grid-levelapplication of energy storage, etc.

A power station for large-scale energy storage by battery may smooth theturbulence of the power output of large-scale new energy powergeneration using a conventional first-order low-pass filter or byfiltering the power output with a variable time constant (T). A delayinherent to the method may sometimes lead to insensitive control. Atarget or objective power output of energy storage output by a commonmobile average filtering algorithm may be limited largely by input powergenerated using the new energy, such as the wind power, the photovoltaicpower, etc. With such an existing method, filtering performance maydecrease in response to sudden change in the power output of the newenergy power generation, impacting a subsequent filtering result.Moreover, in controlling the power output of a power station for energystorage of improved friendliness to the new energy power generation, aconventional method leaves much space for improving capability of smartoptimization based on self-study in terms of adaptive control of theoverall power output of the power station for energy storage.

SUMMARY

Embodiments herein provide a method, system, and storage medium for ADPcontrol by a power station for large-scale energy storage, capable ofreducing impact of incorporation of wind power on a grid and optimizingprotection of capability to work and life of a system for energy storageto improve technical and economic performance of the system for energystorage.

A technical solution herein may be implemented as follows.

A method for Adaptive Dynamic Programming (ADP) control by a powerstation for large-scale energy storage includes:

setting an objective control parameter and a structure of a system forADP control;

performing parameter initialization, and importing an initializedparameter as an initial state of a controlled object;

for a present time point t, computing a rate of turbulence of raw windpower r_(wp) ^(T), smoothing the raw wind power by controlling a rate ofchange, and computing a rate of turbulence of wind and energy storagehybrid power r_(hybrid) ^(T) smoothed, power P_(BESS)(t) of a system forenergy storage, and a State Of Charge (SOC) of the system for energystorage;

performing initialization for training an estimation module and amanagement module;

for each time point, computing and storing a control strategy, the rateof turbulence of the wind and energy storage hybrid power smoothed,power of energy storage, and the SOC of the system for energy storage;and

outputting the rate of turbulence of the wind and energy storage hybridpower smoothed, the power of energy storage, the SOC of the system forenergy storage, and the control strategy for the each time point.

The system for ADP control may include a two-layer structure of theestimation module and the management module.

Each of the estimation module and the management module may be builtwith a three-layer structure of a neural network.

The objective control parameter may include a capacity of wind powergeneration P_(wp) ^(rated), a capacity W_(bat) of the system for energystorage, a limiting range of the SOC of the system for energy storage, asampling interval Δt, an observation time T, an objective rate ofturbulence r_(obj) ^(T), and a limiting rate of turbulence r_(lim) ^(T).

The initialized parameter may include an initial rate of turbulence ofwind power, an initial SOC of the system for energy storage, and actualwind power output at the present time point.

The for a present time point t, computing a rate of turbulence of rawwind power r_(wp) ^(T), smoothing the raw wind power by controlling arate of change, and computing a rate of turbulence of wind and energystorage hybrid power r_(hybrid) ^(T) smoothed, power P_(BESS)(t) of asystem for energy storage, and a State Of Charge (SOC) of the system forenergy storage may include:

for the present time point t, computing the rate of turbulence of theraw wind power r_(wp) ^(T) using formulae of

$\begin{matrix}{{r_{wp}^{T} = {f_{wp}\left( \frac{P_{wp}^{\max} - P_{wp}^{\min}}{P_{wp}^{rated}} \right)}},} & (1) \\{{P_{wp}^{\max} = {\max\left\{ {{P_{wp}(t)},{P_{wp}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{wp}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},} & (2) \\{{{P_{wp}^{\min} = {\min\left\{ {{P_{wp}(t)},{P_{wp}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{wp}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},{and}}\mspace{14mu}} & (3) \\{{T = {n\;\Delta\; t}},} & (4)\end{matrix}$

wherein the P_(wp) ^(rated) is a capacity of wind power, i.e., ratedpower, the P_(wp) ^(max) is a maximal wind power sampled within anobservation time T, the P_(wp) ^(min) is a minimal wind power sampledwithin the observation time, the Δt is a sampling interval, theP_(wp)(t) is the raw wind power, the f_(wp) is a raw function forcomputing a rate of turbulence of the wind power, and the n is a numberof sampling points within the observation time;

smoothing the raw wind power by controlling the rate of change by

defining the rate of change k(t) of the wind power according to the rawwind power P_(wp)(t), a smoothed wind power P_(hybrid)(t), and thesampling interval Δt according to a formula of

$\begin{matrix}{{{k(t)} = \frac{{P_{wp}(t)} - {P_{hybrid}\left( {t - {\Delta\; t}} \right)}}{\Delta\; t}},} & (5)\end{matrix}$

and controlling a rate of turbulence of wind and energy storage hybridpower to be within a required range using the control strategycomprising that:

for k_(hybrid) ^(drop)≤k(t)≤k_(hybrid) ^(rise),P _(hybrid)(t)=P _(wp)(t−Δt)  (6),

for k(t)>k_(hybrid) ^(rise),P _(hybrid)(t)=P _(wp)(t−Δt)+Δt·k _(hybrid) ^(rise)  (7),

and for k(t)<k_(hybrid) ^(drop),P _(hybrid)(t)=P _(wp)(t−Δt)+Δt·k _(hybrid) ^(drop)  (8),

wherein the k_(hybrid) ^(rise) is a limiting rate of change that limitsrise of wind power output, and the k_(hybrid) ^(drop) is a limiting rateof change that limits drop of the wind power output,

with

$\begin{matrix}{{k_{hybrid}^{rise} = \frac{P_{wp}^{rated} \times r_{obj}^{T}}{T}},{and}} & (9) \\{{k_{hybrid}^{drop} = {- \frac{P_{wp}^{rated} \times r_{obj}^{T}}{T}}},} & (10)\end{matrix}$

wherein the r_(obj) ^(T) is an objective rate of turbulence;

computing the rate of turbulence of the wind and energy storage hybridpower r_(hybrid) ^(T) smoothed by controlling the rate of change, byusing formulae of

$\begin{matrix}{\mspace{79mu}{{r_{hybrid}^{T} = {f_{hybrid}\left( \frac{P_{hybrid}^{\max} - P_{hybrid}^{\min}}{P_{hybrid}^{rated}} \right)}},}} & (11) \\{{P_{hybrid}^{\max} = {\max\left\{ {{P_{hybrid}(t)},{P_{hybrid}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{hybrid}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},} & (12) \\{{P_{hybrid}^{\min} = {\min\left\{ {{P_{hybrid}(t)},{P_{hybrid}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{hybrid}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},\mspace{20mu}{and}} & (13) \\{\mspace{79mu}{{T = {n\;\Delta\; t}},}} & (14)\end{matrix}$

wherein the P_(hybrid) ^(max) is a maximal wind and energy storagehybrid power sampled within the observation time T, and the P_(hybrid)^(min) is a minimal wind and energy storage hybrid power sampled withinthe observation time,

wherein an objective of controlling a rate of turbulence of power usingthe change rage is to control the rate of turbulence of the wind andenergy storage hybrid power smoothed to be less than a specifiedobjective rate within the observation time, as shown by a formula ofr _(hybrid) ^(T) <r _(obj) ^(T)  (15),

wherein the r_(obj) ^(T) is the objective rate of turbulence of the windand energy storage hybrid power within the observation time T;

computing the power of the system for energy storage asP _(BESS)(t)=P _(hybrid)(t)−P _(wp)(t)  (16);

and computing the SOC of the energy storage as follows,

wherein for P_(BESS)(t)>0, the system for energy storage is discharged,and the SOC decreases as shown in a formula of

$\begin{matrix}{{{{SOC}(t)} = {{{SOC}\left( {t - {\Delta\; t}} \right)} - \frac{\Delta\; t{{P_{BESS}(t)}}}{W_{bat}}}},} & (17)\end{matrix}$

wherein for P_(BESS)(t)<0, the system for energy storage is charged, andthe SOC increases as shown in a formula of

$\begin{matrix}{{{{SOC}(t)} = {{{SOC}\left( {t - {\Delta\; t}} \right)} + \frac{\Delta\; t{{P_{BESS}(t)}}}{W_{bat}}}},} & (18)\end{matrix}$

wherein the W_(bat) is a capacity of the system for energy storage.

The performing initialization for training an estimation module and amanagement module may include:

initializing a discount factor α, a learning rate l_(a) of themanagement module, a learning rate l_(c) of the estimation module, aweight W_(a) of the management module, a weight W_(c) of the estimationmodule, a maximal number of cycles, and an expected error of moduletraining, by setting an initial weight as a random number within (−1,+1), and selecting and adjusting the learning rates and the discountfactor according to an objective result of control.

The for each time point, computing and storing a control strategy, therate of turbulence of the wind and energy storage hybrid power smoothed,power of energy storage, and the SOC of the system for energy storagemay include steps of:

determining whether the rate of turbulence of the wind and energystorage hybrid power r_(hybrid) ^(T) meets r_(lim) ^(T)≤r_(hybrid)^(T)<r_(obj) ^(T); in response to determining that it does, taking, bythe system for energy storage, no action to correct the power of energystorage; otherwise in response to determining that it does not,performing a next step of ADP module training in seeking an optimalcorrection to the power of energy storage;

training the estimation module by inputting the control strategy and astate of the controlled object to the estimation module, updating theweight of the estimation module, and outputting a cost function;

training the management module by inputting, to the management module,the state of the controlled object including the rate of turbulence ofthe wind and energy storage hybrid power r_(hybrid) ^(T) and the powerof energy storage P_(BESS)(t), updating the weight of the managementmodule, and outputting the control strategy as a correction ΔP_(BESS)(t)to the power of energy storage; and

storing the control strategy for the each time point, computing thestate of the controlled object for a next time point t=t+1 and repeatingthe steps until control ends.

The determining whether the rate of turbulence of the wind and energystorage hybrid power r_(hybrid) ^(T) meets r_(lim) ^(T)≤r_(hybrid)^(T)<r_(obj) ^(T) may include:

in response to determining that 0<r_(hybrid) ^(T)<r_(lim) ^(T),determining that the system for energy storage has been outputting toomuch power and needs reverse correction ofP _(BESS) ^(ADP)(t)=P _(BESS)(t)+ΔP _(BESS)(t)  (22),

wherein the ΔP_(BESS)(t) is the correction to the power of energystorage; and

in response to determining that r_(lim) ^(T)≤r_(hybrid) ^(T)<r_(obj)^(T), determining that the system for energy storage has been outputtingproper power and needs no correction, i.e.,P _(BESS) ^(ADP)(t)=P _(BESS)(t)  (23).

The ΔP_(BESS)(t) may be the power of the system for energy storageacquired by ADP of the power of energy storage by controlling the rateof change.

The training the estimation module by inputting the control strategy anda state of the controlled object to the estimation module, updating theweight of the estimation module, and outputting a cost function mayinclude:

normalizing the state of the controlled object comprising the rate ofturbulence of the wind and energy storage hybrid power r_(hybrid) ^(T)and the control strategy, i.e., the correction ΔP_(BESS)(t) to the powerof energy storage, to be within [−1, +1];

inputting the state of the controlled object comprising the rate ofturbulence of the wind and energy storage hybrid power r_(hybrid) ^(T)and the control strategy, i.e., the correction ΔP_(BESS)(t) to the powerof energy storage, to the estimation module, computing the cost functionJ_(c) ^(WPBESS)(t) output by the estimation module, training theestimation module by building an objective function E_(chybrid) (t), andupdating a weight of a neural network of the estimation module accordingto a weight updating formula of the estimation module, using formulae ofU(t)=[r _(hybrid) ^(T) ,ΔP _(BESS)(t),t]  (24),J _(chybrid)(t)J _(c)[r _(hybrid) ^(T) ,ΔP _(BESS)(t),t,W _(c)]  (25),and

$\begin{matrix}{{{E_{chybrid}(t)} = {\frac{1}{2}\left\lbrack {{J_{chybrid}(t)} - {U\left( {t + 1} \right)} - {\beta_{c}{J_{chybrid}\left( {t + 1} \right)}}} \right\rbrack}^{2}},} & (26)\end{matrix}$

wherein the cost function J_(chybrid)(t) is the output of the estimationmodule, the U(t) is a utility function of r_(hybrid) ^(T), ΔP_(BESS)(t),t defined according to an objective result of control, and the β_(c) isa discount factor; and

updating the weight W_(c) of the neural network of the estimation moduleby training the estimation module via gradient descent or particle swarmoptimization to minimize the objective function E_(chybrid)(t), andending the training in response to determining that the objectivefunction E_(chybrid)(t) has decreased to a set error or a maximal numberof iterations has been reached.

The training the management module by inputting, to the managementmodule, the state of the controlled object comprising the rate ofturbulence of the wind and energy storage hybrid power r_(hybrid) ^(T)and the power of energy storage P_(BESS)(t), updating the weight of themanagement module, and outputting the control strategy as a correctionΔP_(BESS)(t) to the power of energy storage may include:

training the management module by inputting, to the management module,the state of the controlled object comprising the rate of turbulence ofthe wind and energy storage hybrid power r_(hybrid) ^(T) hybrid and thepower of energy storage P_(BESS)(t);

adjusting the control strategy, i.e., the correction ΔP_(BESS)(t) to thepower of energy storage, by minimizing an output J_(chybrid)(t) of theestimation module using formulae ofΔP _(BESS)(t)=u[r _(hybrid) ^(T) ,t,W _(a)]  (27),and

$\begin{matrix}{{{E_{ahybrid}(t)} = {\frac{1}{2}\left\lbrack {J_{chybrid}(t)} \right\rbrack}^{2}},} & (28)\end{matrix}$

wherein the control strategy ΔP_(BESS)(t) is output by the managementmodule to adjust the power of energy storage to vary within a properrange to reduce a range of turbulence of the SOC of the system forenergy storage, and the u indicates that the control strategyΔP_(BESS)(t) is a function of r_(hybrid) ^(T), t, W_(a); and

updating a weight w_(a) of a neural network of the management module bytraining the management module via gradient descent or particle swarmoptimization to minimize an objective function E_(ahybrid)(t), andending the training in response to determining that the objectivefunction E_(ahybrid)(t) has decreased to a set error or a maximal numberof iterations has been reached.

A system for Adaptive Dynamic Programming (ADP) control by a powerstation for large-scale energy storage includes a parameterinitialization module, a data collection and computation module, amanagement module, an estimation module, and an output module.

The parameter initialization module is arranged for: setting anobjective control parameter and a structure of a system for ADP control,and sending the objective control parameter to the data collection andcomputation module.

The data collection and computation module is arranged for: computing arate of turbulence of wind and energy storage hybrid power according tothe objective control parameter, and sending the rate of turbulence ofthe wind and energy storage hybrid power to the management module andthe estimation module.

The management module is arranged for: acquiring a correction to powerof energy storage according to the rate of turbulence of the wind andenergy storage hybrid power, and sending the correction to the power ofenergy storage to the data collection and computation module, themanagement module, and the estimation module.

The estimation module is arranged for: acquiring a cost functionaccording to the rate of turbulence of the wind and energy storagehybrid power, and sending the cost function to the management module.

The output module is arranged for: outputting a control strategy, a rateof turbulence of wind and energy storage hybrid power smoothed, thepower of energy storage, and a State Of Charge (SOC) of a system forenergy storage for each time point.

According to an embodiment herein, a method for Adaptive DynamicProgramming (ADP) control by a power station for large-scale energystorage may include:

for a present time point t, computing a rate of turbulence of raw windpower r_(wp) ^(T), smoothing the raw wind power by controlling a rate ofchange, and computing a rate of turbulence of wind and energy storagehybrid power r_(hybrid) ^(T) smoothed, power P_(BESS)(t) of a system forenergy storage, and a State Of Charge (SOC) of the system for energystorage;

determining whether the r_(hybrid) ^(T), the P_(BESS)(t), and the SOCare within constraint ranges corresponding to an objective controlparameter;

in response to determining that they are not within the constraintranges, inputting the r_(hybrid) ^(T) to the management module for thepresent time point;

for the present time point, outputting, by the management moduleaccording to the r_(hybrid) ^(T), a control strategy for controllingcharging power and discharging power of the system for energy storage ofthe power station for large-scale energy storage;

inputting the r_(hybrid) ^(T) and the control strategy for the presenttime point to the estimation module for the present time point;

outputting, by the estimation module, a cost function according to ther_(hybrid) ^(T) and the control strategy for the present time point;

building, according to the r_(hybrid) ^(T) and the cost function, anobjective function for training the estimation module;

training the estimation module according to the objective function tominimize the objective function; and

training the management module according to the cost function tominimize the cost function. The estimation module and the managementmodule may be retrained for outputting the control strategy for a nexttime point.

The control strategy may include a correction to power of energystorage.

According to an embodiment herein, a computer-readable storage mediummay have stored therein instructions executable by a computer to performan aforementioned method.

ADP-based adaptive optimization of a power station for large-scaleenergy storage by battery is proposed according to embodiments herein.With the method and system according to embodiments herein, effectiveadaptive optimization of overall charging and discharging power of asystem for large-scale energy storage by battery is implemented byconsidering a SOC of a power station for large-scale energy storage bybattery, a feedback rate of turbulence of new energy power generation,an estimation module and a management module based on a neural network,etc. With the technical solution according to embodiments herein, smartoptimization of a control algorithm is performed in real time based onthe neural network, improving capability of self-study and adaptivecontrol of the system. Power output of the system for energy storage isadaptively corrected dynamically in real time to meet a demand forincorporating wind power into the grid. In addition, the SOC of abattery for energy storage is kept within a proper range, implementingproper charging and discharging of the system for energy storage bybattery, implementing optimization of real-time charging and dischargingpower of the system for large-scale energy storage. The method may applyto optimization of charging and discharging power of power stations(system) for energy storage of various scales as well as battery energymanagement.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram of a structure of a system for ADP control by apower station for large-scale energy storage according to an embodimentherein.

FIG. 2 is a flowchart of a method for ADP control by a power station forlarge-scale energy storage according to an embodiment herein.

DETAILED DESCRIPTION

Embodiments herein will be further elaborated below with reference tothe drawings. Note that embodiments below are for illustrating andexplaining the present disclosure, and are not intended to limit thepresent disclosure.

FIG. 1 shows a system for ADP control by a power station for large-scaleenergy storage according to an embodiment herein. The system may includemodules as follows.

The system may include a parameter initialization module. The system forADP control may include a two-layer structure of an estimation moduleand a management module, each of which may be built with a three-layerstructure of a neural network. A parameter of the management module andthe estimation module may include at least one of a discount factor α,network learning rates l_(a) and l_(c), weights W_(a) and W_(c), amaximal number of cycles, an expected error of network training, etc.

An objective control parameter may include at least one of a capacity ofwind power generation P_(wp) ^(rated), a capacity W_(bat) of a systemfor energy storage, a limiting range of a State Of Charge (SOC) of thesystem for energy storage, a sampling interval Δt, an observation timeT, an objective rate of turbulence r_(obj) ^(T), a limiting rate ofturbulence r_(lim) ^(T), etc.

An initial state of a controlled object may include an initial rate ofturbulence of wind power, an initial SOC of the system for energystorage, actual wind power output for a present time point, etc. Thecontrolled object may be the power station for large-scale energystorage.

The system may include a data collection and computation module. Theactual wind power output as well as charging power and discharging powerof the system for energy storage may be acquired. The rate of turbulenceof the wind power and the SOC may be computed in real time. It may bedetermined in real time whether the state parameters are withinconstraint ranges. The charging power and the discharging power of thesystem for energy storage may be adjusted when the state parameters arenot within the constraint ranges.

The estimation module may be trained as follows. The rate of turbulenceof the wind power r_(hybrid) ^(T) and a control strategy, i.e., acorrection ΔP_(BESS)(t) to power of energy storage may be input to theestimation module. A cost function J_(c) ^(WPBESS)(t) output by theestimation module may be computed. The estimation module may be trainedby building an objective function E_(chybrid)(t). The weight of theneural network of the estimation module may be updated according to aweight updating formula of the estimation module. The rate of turbulenceof the wind power r_(hybrid) ^(T) and the control strategy, i.e., thecorrection ΔP_(BESS)(t) to the power of energy storage, may have to benormalized to be within [−1, +1] before being sent into the network forcomputation. The objective function may be built according to the costfunction. After the objective function E_(chybrid)(t) has been built,the estimation module may be retrained by minimizing the objectivefunction, for acquiring, according to the rate of turbulence of the windpower r_(hybrid) ^(T) collected at a next collecting time point and thecontrol strategy output by the management module, the cost function fornext management module training.

The weight W_(c) of the neural network of the estimation module may beupdated by training the estimation module by minimizing the objectivefunction E_(chybrid)(t) The training may end when the objective functionE_(chybrid)(t) has decreased to a set error or a maximal number ofiterations has been reached.

The management module may be trained by minimizing the outputJ_(chybrid)(t) of the estimation module. The weight W_(a) of the neuralnetwork of the management module may be updated according to a weightupdating formula of the management module. The control strategy, i.e.,the correction ΔP_(BESS)(t) to the power of energy storage, may beadjusted by training the management module by minimizing the outputJ_(chybrid)(t) of the estimation module. The weight W_(a) of the neuralnetwork of the management module may be updated by minimizing theobjective function E_(ahybrid)(t). The training may end when theobjective function E_(ahybrid)(t) has decreased to a set error or amaximal number of iterations has been reached.

Note that the objective function E_(chybrid)(t) for training theestimation module and the objective function E_(ahybrid)(t) for trainingthe management module may differ. The E_(ahybrid)(t) may be positivelycorrelated with a value of the cost function. Accordingly, themanagement module may be trained by minimizing the E_(ahybrid)(t).

The system may include an output module. The control strategy for eachtime point may be stored and output in real time. The smoothing processmay be adjusted in real time on line to control the charging power andthe discharging power of the system for energy storage. The chargingpower and the discharging power of the system for energy storage may becontrolled by the control strategy.

To sum up, with the present disclosure, both new energy power generationand a system for large-scale energy storage may be incorporated andoperated in a grid, and optimal charging and discharging power of thesystem for energy storage such as a capacity of the system forlarge-scale energy storage, etc., may be found, by adjusting power of apower station for large-scale energy storage in real time using an ADPalgorithm considering both a rate of turbulence of power output of thenew energy power generation and the SOC of the system for large-scaleenergy storage by battery. An ADP algorithm may be independent of anaccurate mathematical model of a controlled system or process, and becapable of online self-study to adapt to a change of a system parameter,with great robustness. Therefore, with the present disclosure, the poweroutput of the new energy power generation may be smoothed on lineadaptively by adaptive smoothing control with an ADP algorithm,optimizing a result of controlling the system for large-scale energystorage by battery.

FIG. 2 shows a method for ADP control by a power station for large-scaleenergy storage according to an embodiment herein. The method may includesteps as follows.

In step 1, a structure of a system for ADP control, parameters of amanagement module and an estimation module, and objective controlparameter may be set.

The system for ADP control may include a two-layer structure of theestimation module and the management module. Alternatively, the systemfor ADP control may include a three-layer structure of a model module,the estimation module, and the management module. Each module may bebuilt with a three-layer structure of a neural network. A parameter ofthe management module and the estimation module may include at least oneof a discount factor α, network learning rates l_(a) and l_(c), weightsW_(a) and W_(c), a maximal number of cycles, an expected error ofnetwork training, etc. An initial weight may be set as a random numberwithin (−1, +1). The learning rates and the discount factor may beselected and adjusted according to an objective result of control. Aproper model of a neural network, including at least one of a mode oftraining, a parameter, a structure, and a type of the network, etc., maybe selected via coordinated optimization considering particulars of acontrolled object, a convergence speed, an accuracy of a result ofcomputation, etc.

An action network in FIG. 2 may correspond to the neural network of themanagement module herein. an estimation network in FIG. 2 may be theneural network of the estimation module.

An objective control parameter may include at least one of a capacity ofwind power generation P_(wp) ^(rated), a capacity W_(bat) of a systemfor energy storage, a limiting range of a State Of Charge (SOC) of thesystem for energy storage, a sampling interval Δt, an observation timeT, an objective rate of turbulence r_(obj) ^(T), a limiting rate ofturbulence r_(lim) ^(T), etc.

In step 2, parameter initialization may be performed. An initializedparameter may be imported as an initial state of a controlled object.

The initialized parameter may include at least one of an initial rate ofturbulence of wind power, an initial SOC of the system for energystorage, actual wind power output at the present time point, etc.

In step 3, a rate of turbulence of raw wind power r_(wp) ^(T) for apresent time point t may be computed. The raw wind power may be smoothedby controlling a rate of change. A rate of turbulence of wind and energystorage hybrid power r_(hybrid) ^(T) smoothed may be computed. PowerP_(BESS)(t) of a system for energy storage may be computed. A State OfCharge (SOC) of the system for energy storage may be computed.

In step 4, initialization may be performed for training the estimationmodule and the management module.

The rate of turbulence of the raw wind power r_(wp) ^(T) for the presenttime point t may be computed and the raw wind power may be smoothed bycontrolling the rate of change as follows.

The rate of change k(t) of the wind power may be defined according tothe raw wind power P_(wp)(t), a smoothed wind power P_(hybrid)(t), andthe sampling interval Δt according to a formula of

$\begin{matrix}{{k(t)} = {\frac{{P_{wp}(t)} - {P_{hybrid}\left( {t - {\Delta\; t}} \right)}}{\Delta\; t}.}} & (5)\end{matrix}$

A rate of turbulence of wind and energy storage hybrid power may becontrolled to be within a required range using the control strategy asfollows.

For k_(hybrid) ^(drop)≤k(t)≤k_(hybrid) ^(rise),P _(hybrid)(t)=P _(wp)(t−Δt)  (6).

For k(t)≥k_(hybrid) ^(rise),P _(hybrid)(t)=P _(wp)(t−Δt)+Δt·k _(hybrid) ^(rise)  (7).

For k(t)<k_(hybrid) ^(drop),P _(hybrid)(t)=P _(wp)(t−Δt)+Δt·k _(hybrid) ^(drop)  (8).

The k_(hybrid) ^(rise) may be a limiting rate of change that limits riseof wind power output, as defined below. The k_(hybrid) ^(drop) may be alimiting rate of change that limits drop of the wind power output, asdefined below.

$\begin{matrix}{k_{hybrid}^{rise} = {\frac{P_{wp}^{rated} \times r_{obj}^{T}}{T}.}} & (9) \\{k_{hybrid}^{drop} = {- {\frac{P_{wp}^{rated} \times r_{obj}^{T}}{T}.}}} & (10)\end{matrix}$

The rate of turbulence of the wind and energy storage hybrid powerr_(hybrid) ^(T) may be smoothed by controlling the rate of change, usingformulae as follows.

$\begin{matrix}{\mspace{79mu}{r_{hybrid}^{T} = {{f_{hybrid}\left( \frac{P_{hybrid}^{\max} - P_{hybrid}^{\min}}{P_{hybrid}^{rated}} \right)}.}}} & (11) \\{P_{hybrid}^{\max} = {\max{\left\{ {{P_{hybrid}(t)},{P_{hybrid}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{hybrid}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}.}}} & (12) \\{P_{hybrid}^{\min} = {\min{\left\{ {{P_{hybrid}(t)},{P_{hybrid}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{hybrid}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}.}}} & (13) \\{\mspace{79mu}{T = {n\;\Delta\;{t.}}}} & (14)\end{matrix}$

The P_(hybrid) ^(max) may be a maximal wind and energy storage hybridpower sampled within the observation time T. The P_(hybrid) ^(min) maybe a minimal wind and energy storage hybrid power sampled within theobservation time,

A rate of turbulence of power may be controlled using the change rage tocontrol the rate of turbulence of the wind and energy storage hybridpower smoothed to be less than a specified objective rate within theobservation time, as shown by a formula ofr _(hybrid) ^(T) <r _(obj) ^(T)  (15),

The r_(obj) ^(T) may be the objective rate of turbulence of the wind andenergy storage hybrid power within the observation time T.

Charging power and discharging power of the system for energy storagemay be computed according to the above formulae. The power of energystorage for a time point t may be computed according to a formula ofP _(BESS) =P _(hybrid)(t)−P _(wp)(t)  (16).

The SOC of the energy storage may be computed as follows.

For P_(BESS)(t)>0, the system for energy storage may be discharged. TheSOC may decrease according to a formula of

$\begin{matrix}{{{SOC}(t)} = {{{SOC}\left( {t - {\Delta\; t}} \right)} - {\frac{\Delta\; t{{P_{BESS}(t)}}}{W_{bat}}.}}} & (17)\end{matrix}$

For P_(BESS)(t)<0, the system for energy storage may be charged. The SOCmay increase according to a formula of

$\begin{matrix}{{{SOC}(t)} = {{{SOC}\left( {t - {\Delta\; t}} \right)} + {\frac{\Delta\; t{{P_{BESS}(t)}}}{W_{bat}}.}}} & (18)\end{matrix}$

The W_(bat) may be a capacity of the system for energy storage.

In step 5, it may be determined whether the rate of turbulence of thewind and energy storage hybrid power r_(hybrid) ^(T) meets a constraintof r_(lim) ^(T)≤r_(hybrid) ^(T)<r_(obj) ^(T). When it does not meet theconstraint, a next step of ADP network training may be performed to seekan optimal correction to the power of energy storage. When it meets theconstraint, the system for energy storage may take no action to correctthe power of energy storage.

It may be determined whether ADP adjustment or regulation of the powerof energy storage is required as follows. Based on the power of energystorage P_(BESS)(t) of the system for energy storage and the rate ofturbulence of the wind and energy storage hybrid power r_(hybrid) ^(T)acquired by controlling the rate of change, a limiting rate r_(lim) ^(T)may be newly defined to limit the rate of turbulence of the wind andenergy storage hybrid power. The control strategy as follows may beformulated according to the limiting rate and the objective rate ofturbulence r_(obj) ^(T).

For 0<r_(hybrid) ^(T)<r_(lim) ^(T), the system for energy storage hasbeen outputting too much power. Thus reverse correction as follows maybe required.P _(BESS) ^(ADP)(t)=P _(BESS)(t)+ΔP _(BESS)  (22).

For r_(lim) ^(T)≤r_(hybrid) ^(T)<r_(obj) ^(T), the system for energystorage has been outputting proper power. Thus no correction may benecessary.P _(BESS) ^(ADP)(t)≤P _(BESS)(t)  (23).

The P_(BESS) ^(ADP)(t) may be the power of the system for energy storageacquired by ADP of the power of energy storage by controlling the rateof change.

In step 6, a management network may be trained by inputting, to themanagement network, the state of the controlled object, i.e., the rateof turbulence of the wind and energy storage hybrid power r_(hybrid)^(T), the power of energy storage P_(BESS)(t), etc. The weight of themanagement network may be updated. The control strategy, i.e., acorrection ΔP_(BESS)(t) to the power of energy storage, may be output.

The management network may be trained as follows.

The management module may be trained by minimizing the outputJ_(chybrid)(t) of the estimation module. The weight W_(a) of the neuralnetwork of the management module may be updated according to a weightupdating formula of the management module. The control strategy, i.e.,the correction ΔP_(BESS)(t) to the power of energy storage, may beadjusted by training the management module by minimizing the outputJ_(chybrid)(t) of the estimation module. The weight W_(a) of the neuralnetwork of the management module may be updated by minimizing theobjective function E_(ahybrid)(t). The training may end when theobjective function E_(ahybrid)(t) has decreased to a set error or amaximal number of iterations has been reached.

In step 7, an estimation network may be trained by inputting the controlstrategy and a state of the controlled object to the estimation network.The weight of the estimation network may be updated. A cost function maybe output.

The estimation network may be trained as follows.

The rate of turbulence of the wind power r_(hybrid) ^(T) and a controlstrategy, i.e., a correction ΔP_(BESS)(t) to power of energy storage maybe input to the estimation module. A cost function J_(c) ^(WPBESS)(t)output by the estimation module may be computed. The estimation modulemay be trained by building an objective function E_(chybrid)(t). Theweight of the neural network of the estimation module may be updatedaccording to a weight updating formula of the estimation module. Therate of turbulence of the wind power r_(hybrid) ^(T) and the controlstrategy, i.e., the correction ΔP_(BESS)(t) to the power of energystorage, may have to be normalized to be within [−1, +1] before beingsent into the network for computation.

The weight W_(c) of the neural network of the estimation module may beupdated by training the estimation module by minimizing the objectivefunction E_(chybrid)(t). The training may end when the objectivefunction E_(chybrid)(t) has decreased to a set error or a maximal numberof iterations has been reached.

In step 8, the control strategy for the time may be stored. The state ofthe controlled object for a next time point t=t+1 may be computed. Thesteps 5-7 may be repeated.

In step 9, the steps may be cycled until control ends. The rate ofturbulence of the wind and energy storage hybrid power r_(hybrid) ^(ADP)smoothed, the power of energy storage P_(BESS) ^(ADP)(t), the SOC of thesystem for energy storage, the control strategy for each time point,etc., may be output.

According to an embodiment herein, a method for ADP control by a powerstation for large-scale energy storage may include steps as follows.

A rate of turbulence of raw wind power r_(wp) ^(T) for a present timepoint t may be computed. The raw wind power may be smoothed bycontrolling a rate of change. A rate of turbulence of wind and energystorage hybrid power r_(hybrid) ^(T) smoothed may be computed. PowerP_(BESS)(t) of a system for energy storage may be computed. A State OfCharge (SOC) of the system for energy storage may be computed.

It may be determined whether the r_(hybrid) ^(T), the P_(BESS)(t), andthe SOC are within constraint ranges corresponding to an objectivecontrol parameter.

When they are not within the constraint ranges, the r_(hybrid) ^(T) maybe input to the management module for the present time point.

The management module for the present time point may output a controlstrategy according to the r_(hybrid) ^(T). The control strategy may befor controlling charging power and discharging power of the system forenergy storage of the power station for large-scale energy storage.

The r_(hybrid) ^(T) and the control strategy for the present time pointmay be input to the estimation module for the present time point.

The estimation module may output a cost function according to ther_(hybrid) ^(T) and the control strategy for the present time point.

An objective function for training the estimation module may be builtaccording to the r_(hybrid) ^(T) and the cost function.

The estimation module may be trained according to the objective functionby minimizing the objective function.

The management module may be trained according to the cost function byminimizing the cost function. The estimation module and the managementmodule may be retrained for outputting the control strategy for a nexttime point.

The P_(BESS)(t) may include the charging power and the dischargingpower. It may be determined that the P_(BESS)(t) is within theconstraint range thereof when the charging power is no less than minimalcharging power and no greater than maximal charging power allowed, andthe discharging power is no less than minimal discharging power and nogreater than maximal discharging power allowed. The minimal chargingpower and/or the minimal discharging power may be 0.

It may be determined that the SOC is within the constraint range thereofwhen the SOC is no greater than a maximal SOC allowed for the system forenergy storage in work, and no less than a minimal SOC allowed for thesystem for energy storage in work.

It may be determined that the r_(hybrid) ^(T) is within the constraintrange thereof when the r_(hybrid) ^(T) is less than the objective rate.It may be determined that no adjustment is necessary and no controlstrategy may have to be output only when all three parameters are withinthe respective constraint ranges thereof. Otherwise one or more of theseparameters may be input to the management module for the present timepoint to output the control strategy for the present time point forcontrolling charging power and discharging power of the system forenergy storage, and retraining the management module and the estimationmodule, so as to facilitate subsequent more accurate control of thesystem for energy storage of the power station for large-scale energystorage.

The control strategy may include a correction to power of energystorage. The system for energy storage of the power station forlarge-scale energy storage may adjust the charging power and thedischarging power per se according to the correction to the power ofenergy storage.

According to an embodiment herein, a computer-readable storage mediummay have stored therein instructions executable by a computer to performany aforementioned method.

The computer-readable storage medium herein may be various types ofstorage media. The computer-readable storage medium herein may be anon-transitory storage medium.

As shown in FIG. 1, a system for ADP control by a power station forlarge-scale energy storage may include at least one of a parameterinitialization module, a data collection and computation module, amanagement module, an estimation module, an output module, etc.

The parameter initialization module may be arranged for: setting anobjective control parameter and a structure of a system for ADP control,and sending the objective control parameter to the data collection andcomputation module.

The data collection and computation module may be arranged for:computing a rate of turbulence of wind and energy storage hybrid poweraccording to the objective control parameter, and sending the rate ofturbulence of the wind and energy storage hybrid power to the managementmodule and the estimation module.

The management module may be arranged for: acquiring a correction topower of energy storage according to the rate of turbulence of the windand energy storage hybrid power, and sending the correction to the powerof energy storage to the data collection and computation module, themanagement module, and the estimation module.

The estimation module may be arranged for: acquiring a cost functionaccording to the rate of turbulence of the wind and energy storagehybrid power, and sending the cost function to the management module.

The output module may be arranged for: outputting a control strategy, arate of turbulence of wind and energy storage hybrid power smoothed, thepower of energy storage, and a State Of Charge (SOC) of a system forenergy storage for each time point.

Note that embodiments herein are for illustrating the present disclosureinstead of limiting the present disclosure. Any modification madeaccording to the principle of the present disclosure shall be deemed tofall within the scope of the present disclosure.

The invention claimed is:
 1. A method for Adaptive Dynamic Programming(ADP) control by a power station for large-scale energy storage,comprising: setting an objective control parameter and a structure of asystem for ADP control; performing parameter initialization, andimporting an initialized parameter as an initial state of a controlledobject; for a present time point t, computing a rate of turbulence ofraw wind power r_(wp) ^(T), smoothing the raw wind power by controllinga rate of change, and computing a rate of turbulence of wind and energystorage hybrid power r_(hybrid) ^(T) smoothed, power P_(BESS)(t) of asystem for energy storage, and a State Of Charge (SOC) of the system forenergy storage; performing initialization for training an estimationmodule and a management module; for each time point, computing andstoring a control strategy, the rate of turbulence of the wind andenergy storage hybrid power smoothed, power of energy storage, and theSOC of the system for energy storage; and outputting the rate ofturbulence of the wind and energy storage hybrid power smoothed, thepower of energy storage, the SOC of the system for energy storage, andthe control strategy for the each time point, wherein the system for ADPcontrol comprises a two-layer structure of the estimation module and themanagement module, wherein each of the estimation module and themanagement module is built with a three-layer structure of a neuralnetwork, wherein the objective control parameter comprises a capacity ofwind power generation P_(wp) ^(rated), a capacity W_(bat) of the systemfor energy storage, a limiting range of the SOC of the system for energystorage, a sampling interval Δt, an observation time T, an objectiverate of turbulence r_(obj) ^(T), and a limiting rate of turbulencer_(lim) ^(T).
 2. The method according to claim 1, wherein theinitialized parameter comprises an initial rate of turbulence of windpower, an initial SOC of the system for energy storage, and actual windpower output at the present time point.
 3. The method according to claim1, wherein the for a present time point t, computing a rate ofturbulence of raw wind power r_(wp) ^(T), smoothing the raw wind powerby controlling a rate of change, and computing a rate of turbulence ofwind and energy storage hybrid power hybrid r_(hybrid) ^(T) smoothed,power P_(PBESS)(t) of a system for energy storage, and a State Of Charge(SOC) of the system for energy storage comprises: for the present timepoint t, computing the rate of turbulence of the raw wind power r_(wp)^(T) using formulae of $\begin{matrix}{\mspace{11mu}{{r_{wp}^{T} = {f_{wp}\left( \frac{P_{wp}^{\max} - P_{wp}^{\min}}{P_{wp}^{rated}} \right)}},}} & (1) \\{{P_{wp}^{\max} = {\max\left\{ {{P_{wp}(t)},{P_{wp}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{wp}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},} & (2) \\{{P_{wp}^{\min} = {\min\left\{ {{P_{wp}(t)},{P_{wp}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{wp}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},{and}} & (3) \\{{T = {n\;\Delta\; t}},} & (4)\end{matrix}$ wherein the P_(wp) ^(rated) is a capacity of wind power,i.e., rated power, the P_(wp) ^(max) is a maximal wind power sampledwithin an observation time T, the P_(wp)(t) is a minimal wind powersampled within the observation time, the Δt is a sampling interval, theP_(wp)(t) is the raw wind power, the f_(up) is a raw function forcomputing a rate of turbulence of the wind power, and the n is a numberof sampling points within the observation time; smoothing the raw windpower by controlling the rate of change by defining the rate of changek(t) of the wind power according to the raw wind power P_(wp)(t), asmoothed wind power P_(hybrid)(t), and the sampling interval Δtaccording to a formula of $\begin{matrix}{{{k(t)} = \frac{{P_{wp}(t)} - {P_{hybrid}\left( {t - {\Delta\; t}} \right)}}{\Delta\; t}},} & (5)\end{matrix}$ and controlling a rate of turbulence of wind and energystorage hybrid power to be within a required range using the controlstrategy comprising that: for k_(hybrid) ^(drop)≤k(t)≤k_(hybrid)^(rise),P _(hybrid)(t)=P _(wp)(t−Δt)  (6), for k(t)>k_(hybrid) ^(rise),P _(hybrid)(t)=P _(wp)(t−Δt)+Δt·k _(hybrid) ^(rise)  (7), and fork(t)<k_(hybrid) ^(drop),P _(hybrid)(t)=P _(wp)(t−Δt)+Δt·k _(hybrid) ^(drop)  (8), wherein thek_(hybrid) ^(rise) is a limiting rate of change that limits rise of windpower output, and the k_(hybrid) ^(drop) is a limiting rate of changethat limits drop of the wind power output, with $\begin{matrix}{{k_{hybrid}^{rise} = \frac{P_{wp}^{rated} \times r_{obj}^{T}}{T}},{and}} & (9) \\{{k_{hybrid}^{drop} = {- \frac{P_{wp}^{rated} \times r_{obj}^{T}}{T}}},} & (10)\end{matrix}$ wherein the r_(obj) ^(T) is an objective rate ofturbulence; computing the rate of turbulence of the wind and energystorage hybrid power r_(hybrid) ^(T) smoothed by controlling the rate ofchange, by using formulae of $\begin{matrix}{\mspace{79mu}{{r_{hybrid}^{T} = {f_{hybrid}\left( \frac{P_{hybrid}^{\max} - P_{hybrid}^{\min}}{P_{hybrid}^{rated}} \right)}},}} & (11) \\{{P_{hybrid}^{\max} = {\max\left\{ {{P_{hybrid}(t)},{P_{hybrid}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{hybrid}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},} & (12) \\{{P_{hybrid}^{\min} = {\min\left\{ {{P_{hybrid}(t)},{P_{hybrid}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{hybrid}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},\mspace{20mu}{and}} & (13) \\{\mspace{76mu}{{T = {n\;\Delta\; t}},}} & (14)\end{matrix}$ wherein the P_(hybrid) ^(max) is a maximal wind and energystorage hybrid power sampled within the observation time T, and theP_(hybrid) ^(min) is a minimal wind and energy storage hybrid powersampled within the observation time, wherein an objective of controllinga rate of turbulence of power using the change rage is to control therate of turbulence of the wind and energy storage hybrid power smoothedto be less than a specified objective rate within the observation time,as shown by a formula ofr_(hybrid) ^(T)<r_(obj) ^(T)  (15), wherein the r_(obj) ^(T) is theobjective rate of turbulence of the wind and energy storage hybrid powerwithin the observation time T; computing the power of the system forenergy storage asP _(BESS)(t)=P _(hybrid)(t)−P _(wp)(t)  (16); and computing the SOC ofthe energy storage as follows, wherein for P_(BESS)(t)>0, the system forenergy storage is discharged, and the SOC decreases as shown in aformula of $\begin{matrix}{{{{SOC}(t)} = {{{SOC}\left( {t - {\Delta\; t}} \right)} - \frac{\Delta\; t{{P_{BESS}(t)}}}{W_{bat}}}},} & (17)\end{matrix}$ wherein for P_(BESS)(t)<0, the system for energy storageis charged, and the SOC increases as shown in a formula of$\begin{matrix}{{{{SOC}(t)} = {{{SOC}\left( {t - {\Delta\; t}} \right)} + \frac{\Delta\; t{{P_{BESS}(t)}}}{W_{bat}}}},} & (18)\end{matrix}$ wherein the W_(bat) is a capacity of the system for energystorage.
 4. The method according to claim 1, wherein the performinginitialization for training an estimation module and a management modulecomprises: initializing a discount factor α, a learning rate l_(a) ofthe management module, a learning rate l_(c) of the estimation module, aweight W_(a) of the management module, a weight W_(c) of the estimationmodule, a maximal number of cycles, and an expected error of moduletraining, by setting an initial weight as a random number within (−1,+1), and selecting and adjusting the learning rates and the discountfactor according to an objective result of control.
 5. The methodaccording to claim 1, wherein the for each time point, computing andstoring a control strategy, the rate of turbulence of the wind andenergy storage hybrid power smoothed, power of energy storage, and theSOC of the system for energy storage comprises steps of: determiningwhether the rate of turbulence of the wind and energy storage hybridpower r_(hybrid) ^(T) meets r_(lim) ^(T)≤r_(hybrid) ^(T)<r_(obj) ^(T);in response to determining that it does, taking, by the system forenergy storage, no action to correct the power of energy storage;otherwise in response to determining that it does not, performing a nextstep of ADP module training in seeking an optimal correction to thepower of energy storage; training the estimation module by inputting thecontrol strategy and a state of the controlled object to the estimationmodule, updating the weight of the estimation module, and outputting acost function; training the management module by inputting, to themanagement module, the state of the controlled object comprising therate of turbulence of the wind and energy storage hybrid powerr_(hybrid) ^(T) and the power of energy storage P_(BESS)(t), updatingthe weight of the management module, and outputting the control strategyas a correction ΔP_(BESS)(t) to the power of energy storage; and storingthe control strategy for the each time point, computing the state of thecontrolled object for a next time point t=t+1 and repeating the stepsuntil control ends.
 6. The method according to claim 5, wherein thedetermining whether the rate of turbulence of the wind and energystorage hybrid power r_(hybrid) ^(T) meets r_(lim) ^(T)≤r_(hybrid) ^(T)r_(obj) ^(T) comprises: in response to determining that 0<r_(hybrid)^(T)<r_(lim) ^(T), determining that the system for energy storage hasbeen outputting too much power and needs reverse correction ofP _(BESS) ^(ADP) s(t)=P _(BESS)(t)+ΔP _(BESS)(t)  (22), wherein theΔP_(BESS)(t) is the correction to the power of energy storage; and inresponse to determining that r_(lim) ^(T)≤r_(hybrid) ^(T)<r_(obj) ^(T),determining that the system for energy storage has been outputtingproper power and needs no correction, i.e.,P _(BESS) ^(ADP)(t)=P _(BESS)(t)  (23), wherein the P_(BESS) ^(ADP)(t)is the power of the system for energy storage acquired by ADP of thepower of energy storage by controlling the rate of change.
 7. The methodaccording to claim 5, wherein the training the estimation module byinputting the control strategy and a state of the controlled object tothe estimation module, updating the weight of the estimation module, andoutputting a cost function comprises: normalizing the state of thecontrolled object comprising the rate of turbulence of the wind andenergy storage hybrid power r_(hybrid) ^(T) and the control strategy,i.e., the correction ΔP_(BESS)(t) to the power of energy storage, to bewithin [−1, +1]; inputting the state of the controlled object comprisingthe rate of turbulence of the wind and energy storage hybrid powerr_(hybrid) ^(T) and the control strategy, i.e., the correctionΔP_(BESS)(t) to the power of energy storage, to the estimation module,computing the cost function J_(c) ^(WPBESS)(t) output by the estimationmodule, training the estimation module by building an objective functionE_(chybrid)(t), and updating a weight of a neural network of theestimation module according to a weight updating formula of theestimation module, using formulae ofU(t)=[r _(hybrid) ^(T) ,ΔP _(BESS)(t),t]  (24),J _(chybrid)(t)=J _(c)[r _(hybrid) ^(T) ,ΔP _(BESS)(t),t,W _(c)]  (25),and $\begin{matrix}{{{E_{chybrid}(t)} = {\frac{1}{2}\left\lbrack {{J_{chybrid}(t)} - {U\left( {t + 1} \right)} - {\beta_{c}{J_{chybrid}\left( {t + 1} \right)}}} \right\rbrack}^{2}},} & (26)\end{matrix}$ wherein the cost function J_(chybrid)(t) is the output ofthe estimation module, the U(t) is a utility function of r_(hybrid)^(T), ΔP_(BESS)(t), t defined according to an objective result ofcontrol, and the β_(c) is a discount factor; and updating the weightW_(c) of the neural network of the estimation module by training theestimation module via gradient descent or particle swarm optimization tominimize the objective function E_(chybrid) (t), and ending the trainingin response to determining that the objective function E_(chybrid) (t)has decreased to a set error or a maximal number of iterations has beenreached.
 8. The method according to claim 5, wherein the training themanagement module by inputting, to the management module, the state ofthe controlled object comprising the rate of turbulence of the wind andenergy storage hybrid power r_(hybrid) ^(T) and the power of energystorage P_(BESS)(t), updating the weight of the management module, andoutputting the control strategy as a correction ΔP_(BESS)(t) BESS to thepower of energy storage comprises: training the management module byinputting, to the management module, the state of the controlled objectcomprising the rate of turbulence of the wind and energy storage hybridpower r_(hybrid) ^(T) and the power of energy storage P_(BESS)(t);adjusting the control strategy, i.e., the correction ΔP_(BESS)(t) to thepower of energy storage, by minimizing an output J_(chybrid)(t) of theestimation module using formulae ofΔP _(BESS)(t)=u[r _(hybrid) ^(T) ,t,W _(a)]  (27), andE _(ahybrid)(t)=½[J _(chybrid)(t)]²  (28), wherein the control strategyΔP_(BESS)(t) is output by the management module to adjust the power ofenergy storage to vary within a proper range to reduce a range ofturbulence of the SOC of the system for energy storage, and the uindicates that the control strategy ΔP_(BESS)(t) is a function ofr_(hybrid) ^(T), W_(a); and updating a weight W_(a) of a neural networkof the management module by training the management module via gradientdescent or particle swarm optimization to minimize an objective functionE_(ahybrid)(t), and ending the training in response to determining thatthe objective function E_(ahybrid)(t) has decreased to a set error or amaximal number of iterations has been reached.
 9. A system for AdaptiveDynamic Programming (ADP) control by a power station for large-scaleenergy storage, comprising: a processor; and memory storing instructionsexecutable by the processor, wherein the processor is arranged for:setting an objective control parameter and a structure of a system forADP control; performing parameter initialization, and importing aninitialized parameter as an initial state of a controlled object; for apresent time point t, computing a rate of turbulence of raw wind powerr_(wp) ^(T), smoothing the raw wind power by controlling a rate ofchange, and computing a rate of turbulence of wind and energy storagehybrid power r_(hybrid) ^(T) smoothed, power P_(BESS)(t) of a system forenergy storage, and a State Of Charge (SOC) of the system for energystorage; performing initialization for training an estimation module anda management module; for each time point, computing and storing acontrol strategy, the rate of turbulence of the wind and energy storagehybrid power smoothed, power of energy storage, and the SOC of thesystem for energy storage; and outputting the rate of turbulence of thewind and energy storage hybrid power smoothed, the power of energystorage, the SOC of the system for energy storage, and the controlstrategy for the each time point, wherein the system for ADP controlcomprises a two-layer structure of the estimation module and themanagement module, wherein each of the estimation module and themanagement module is built with a three-layer structure of a neuralnetwork, wherein the objective control parameter comprises a capacity ofwind power generation P_(wp) ^(rated), a capacity W_(bat) of the systemfor energy storage, a limiting range of the SOC of the system for energystorage, a sampling interval Δt , an observation time T, an objectiverate of turbulence r_(obj) ^(T), and a limiting rate of turbulencer_(lim) ^(T).
 10. The system according to claim 9, wherein the systemfor ADP control comprises a two-layer structure of the estimation moduleand the management module, wherein each of the estimation module and themanagement module is built with a three-layer structure of a neuralnetwork, wherein the objective control parameter comprises a capacity ofwind power generation P_(wp) ^(rated), a capacity W_(bat) of the systemfor energy storage, a limiting range of the SOC of the system for energystorage, a sampling interval Δt, an observation time T, an objectiverate of turbulence r_(obj) ^(T), and a limiting rate of turbulence. 11.The system according to claim 9, wherein the initialized parametercomprises an initial rate of turbulence of wind power, an initial SOC ofthe system for energy storage, and actual wind power output at thepresent time point.
 12. The system according to claim 9, wherein the fora present time point t, computing a rate of turbulence of raw wind powerr_(wp) ^(T), smoothing the raw wind power by controlling a rate ofchange, and computing a rate of turbulence of wind and energy storagehybrid power r_(hybrid) ^(T) smoothed, power P_(BESS)(t) of a system forenergy storage, and a State Of Charge (SOC) of the system for energystorage comprises: for the present time point t, computing the rate ofturbulence of the raw wind power r_(wp) ^(T) using formulae of$\begin{matrix}{\mspace{11mu}{{r_{wp}^{T} = {f_{wp}\left( \frac{P_{wp}^{\max} - P_{wp}^{\min}}{P_{wp}^{rated}} \right)}},}} & (1) \\{{P_{wp}^{\max} = {\max\left\{ {{P_{wp}(t)},{P_{wp}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{wp}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},} & (2) \\{{P_{wp}^{\min} = {\min\left\{ {{P_{wp}(t)},{P_{wp}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{wp}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},{and}} & (3) \\{{T = {n\;\Delta\; t}},} & (4)\end{matrix}$ wherein the P_(wp) ^(rated) is a capacity of wind power,i.e., rated power, the P_(wp) ^(max) is a maximal wind power sampledwithin an observation time T, the P_(wp) ^(min) is a minimal wind powersampled within the observation time, the Δt is a sampling interval, theP_(wp)(t) is the raw wind power, the f_(wp) is a raw function forcomputing a rate of turbulence of the wind power, and the n is a numberof sampling points within the observation time; smoothing the raw windpower by controlling the rate of change by defining the rate of changek(t) of the wind power according to the raw wind power P_(wp)(t), asmoothed wind power P_(hybrid)(t), and the sampling interval Δtaccording to a formula of $\begin{matrix}{{{k(t)} = \frac{{P_{wp}(t)} - {P_{hybrid}\left( {t - {\Delta\; t}} \right)}}{\Delta\; t}},} & (5)\end{matrix}$ and controlling a rate of turbulence of wind and energystorage hybrid power to be within a required range using the controlstrategy comprising that: for k_(hybrid) ^(drop)≤(t)≤k_(hybrid) ^(rise),P _(hybrid)(t)=P _(wp)(t−Δt)  (6), for k(t)>k_(hybrid) ^(rise),P _(hybrid)(t)=P _(wp)(t−Δt)+Δt·k _(hybrid) ^(rise)  (7), and fork(t)<k_(hybrid) ^(drop),P _(hybrid)(t)=P _(wp)(t−Δt)+Δt·k _(hybrid) ^(drop)  (8), wherein thek_(hybrid) ^(rise) is a limiting rate of change that limits rise of windpower output, and the k_(hybrid) ^(drop) is a limiting rate of changethat limits drop of the wind power output, with $\begin{matrix}{{k_{hybrid}^{rise} = \frac{P_{wp}^{rated} \times r_{obj}^{T}}{T}},{and}} & (9) \\{{k_{hybrid}^{drop} = {- \frac{P_{wp}^{rated} \times r_{obj}^{T}}{T}}},} & (10)\end{matrix}$ wherein the r_(obj) ^(T) is an objective rate ofturbulence; computing the rate of turbulence of the wind and energystorage hybrid power r_(hybrid) ^(T) smoothed by controlling the rate ofchange, by using formulae of $\begin{matrix}{\mspace{79mu}{{r_{hybrid}^{T} = {f_{hybrid}\left( \frac{P_{hybrid}^{\max} - P_{hybrid}^{\min}}{P_{hybrid}^{rated}} \right)}},}} & (11) \\{{P_{hybrid}^{\max} = {\max\left\{ {{P_{hybrid}(t)},{P_{hybrid}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{hybrid}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},} & (12) \\{{{P_{hybrid}^{\min} = {\min\left\{ {{P_{hybrid}(t)},{P_{hybrid}\left( {t - {\Delta\; t}} \right)},\ldots\mspace{14mu},{P_{hybrid}\left( {t - {\left( {n - 1} \right)\Delta\; t}} \right)}} \right\}}},\mspace{20mu}{and}}\mspace{11mu}} & (13) \\{\mspace{70mu}{{T = {n\;\Delta\; t}},}} & (14)\end{matrix}$ wherein the P_(hybrid) ^(max) is a maximal wind and energystorage hybrid power sampled within the observation time T, and theP_(hybrid) ^(min) is a minimal wind and energy storage hybrid powersampled within the observation time, wherein an objective of controllinga rate of turbulence of power using the change rage is to control therate of turbulence of the wind and energy storage hybrid power smoothedto be less than a specified objective rate within the observation time,as shown by a formula ofr_(hybrid) ^(T)<r_(obj) ^(T)  (15), wherein the r_(obj) ^(T) is theobjective rate of turbulence of the wind and energy storage hybrid powerwithin the observation time T; computing the power of the system forenergy storage asP _(BESS)(t)=P _(hybrid)(t)−P _(wp)(t)  (16); and computing the SOC ofthe energy storage as follows, wherein for P_(BESS)(t)>0, the system forenergy storage is discharged, and the SOC decreases as shown in aformula of $\begin{matrix}{{{{SOC}(t)} = {{{SOC}\left( {t - {\Delta\; t}} \right)} - \frac{\Delta\; t{{P_{BESS}(t)}}}{W_{bat}}}},} & (17)\end{matrix}$ wherein for P_(BESS)(t)<0, the system for energy storageis charged, and the SOC increases as shown in a formula of$\begin{matrix}{{{{SOC}(t)} = {{{SOC}\left( {t - {\Delta\; t}} \right)} + \frac{\Delta\; t{{P_{BESS}(t)}}}{W_{bat}}}},} & (18)\end{matrix}$ wherein the W_(bat) is a capacity of the system for energystorage.
 13. The system according to claim 9, wherein the performinginitialization for training an estimation module and a management modulecomprises: initializing a discount factor α, a learning rate l_(a) ofthe management module, a learning rate l_(c) of the estimation module, aweight W_(a) of the management module, a weight W_(c) of the estimationmodule, a maximal number of cycles, and an expected error of moduletraining, by setting an initial weight as a random number within (−1,+1), and selecting and adjusting the learning rates and the discountfactor according to an objective result of control.
 14. The systemaccording to claim 9, wherein the for each time point, computing andstoring a control strategy, the rate of turbulence of the wind andenergy storage hybrid power smoothed, power of energy storage, and theSOC of the system for energy storage comprises steps of: determiningwhether the rate of turbulence of the wind and energy storage hybridpower r_(hybrid) ^(T) meets r_(lim) ^(T)≤r_(hybrid) ^(T)<r_(obj) ^(T);in response to determining that it does, taking, by the system forenergy storage, no action to correct the power of energy storage;otherwise in response to determining that it does not, performing a nextstep of ADP module training in seeking an optimal correction to thepower of energy storage; training the estimation module by inputting thecontrol strategy and a state of the controlled object to the estimationmodule, updating the weight of the estimation module, and outputting acost function; training the management module by inputting, to themanagement module, the state of the controlled object comprising therate of turbulence of the wind and energy storage hybrid powerr_(hybrid) ^(T) and the power of energy storage P_(BESS)(t), updatingthe weight of the management module, and outputting the control strategyas a correction ΔP_(BESS)(t) to the power of energy storage; and storingthe control strategy for the each time point, computing the state of thecontrolled object for a next time point t=t+1 and repeating the stepsuntil control ends.
 15. The system according to claim 14, wherein thedetermining whether the rate of turbulence of the wind and energystorage hybrid power r_(hybrid) ^(T) meets r_(lim) ^(T)≤r_(hybrid)^(T)<r_(obj) ^(T) comprises: in response to determining that0<r_(hybrid) ^(T)<r_(lim) ^(T), determining that the system for energystorage has been outputting too much power and needs reverse correctionofP _(BESS) ^(ADP)(t)=P _(BESS)(t)+ΔP _(BESS)(t)  (22), wherein theΔP_(BESS)(t) is the correction to the power of energy storage; and inresponse to determining that r_(lim) ^(T)≤r_(hybrid) ^(T)<r_(obj) ^(T),determining that the system for energy storage has been outputtingproper power and needs no correction, i.e.,P _(BESS) ^(ADP)(t)=P _(BESS)(t)  (23), wherein the P_(BESS) ^(ADP)(t)is the power of the system for energy storage acquired by ADP of thepower of energy storage by controlling the rate of change.
 16. A methodfor Adaptive Dynamic Programming (ADP) control by a power station forlarge-scale energy storage, comprising: for a present time point t,computing a rate of turbulence of raw wind power r_(wp) ^(T), smoothingthe raw wind power by controlling a rate of change, and computing a rateof turbulence of wind and energy storage hybrid power r_(hybrid) ^(T)smoothed, power P_(BESS)(t) of a system for energy storage, and a StateOf Charge (SOC) of the system for energy storage; determining whetherthe r_(hybrid) ^(T), the P_(BESS)(t), and the SOC are within constraintranges corresponding to an objective control parameter; in response todetermining that they are not within the constraint ranges, inputtingthe r_(hybrid) ^(T) to the management module for the present time point;for the present time point, outputting, by the management moduleaccording to the r_(hybrid) ^(T), a control strategy for controllingcharging power and discharging power of the system for energy storage ofthe power station for large-scale energy storage; inputting ther_(hybrid) ^(T) and the control strategy for the present time point tothe estimation module for the present time point; outputting, by theestimation module, a cost function according to the r_(hybrid) ^(T) andthe control strategy for the present time point; building, according tothe r_(hybrid) ^(T) and the cost function, an objective function fortraining the estimation module; training the estimation module accordingto the objective function to minimize the objective function; andtraining the management module according to the cost function tominimize the cost function, wherein the estimation module and themanagement module are retrained for outputting the control strategy fora next time point.
 17. The method according to claim 16, wherein thecontrol strategy comprises a correction to power of energy storage. 18.A non-tranistory computer-readable storage medium having stored thereininstructions executable by a computer to perform a method for AdaptiveDynamic Programming (ADP) control by a power station for large-scaleenergy storage, the method comprising: setting an objective controlparameter and a structure of a system for ADP control; performingparameter initialization, and importing an initialized parameter as aninitial state of a controlled object; for a present time point t,computing a rate of turbulence of raw wind power r_(wp) ^(T), smoothingthe raw wind power by controlling a rate of change, and computing a rateof turbulence of wind and energy storage hybrid power r_(hybrid) ^(T)smoothed, power P_(BESS)(t) of a system for energy storage, and a StateOf Charge (SOC) of the system for energy storage; performinginitialization for training an estimation module and a managementmodule; for each time point, computing and storing a control strategy,the rate of turbulence of the wind and energy storage hybrid powersmoothed, power of energy storage, and the SOC of the system for energystorage; and outputting the rate of turbulence of the wind and energystorage hybrid power smoothed, the power of energy storage, the SOC ofthe system for energy storage, and the control strategy for the eachtime point, wherein the system for ADP control comprises a two-layerstructure of the estimation module and the management module, whereineach of the estimation module and the management module is built with athree-layer structure of a neural network, wherein the objective controlparameter comprises a capacity of wind power generation P_(wp) ^(rated),a capacity W_(bat) of the system for energy storage, a limiting range ofthe SOC of the system for energy storage, a sampling interval Δt, anobservation time T, an objective rate of turbulence r_(obj) ^(T), and alimiting rate of turbulence r_(lim) ^(T).
 19. A non-transitorycomputer-readable storage medium having stored therein instructionsexecutable by a computer to perform a method for Adaptive DynamicProgramming (ADP) control by a power station for large-scale energystorage, the method comprising: for a present time point t, computing arate of turbulence of raw wind power r_(wp) ^(T), smoothing the raw windpower by controlling a rate of change, and computing a rate ofturbulence of wind and energy storage hybrid power r_(hybrid) ^(T)smoothed, power P_(BESS)(t) of a system for energy storage, and a StateOf Charge (SOC) of the system for energy storage; determining whetherthe r_(hybrid) ^(T), P_(BESS)(t), and the SOC are within constraintranges corresponding to an objective control parameter; in response todetermining that they are not within the constraint ranges, inputtingthe r_(hybrid) ^(T) to the management module for the present time point;for the present time point, outputting, by the management moduleaccording to the r_(hybrid) ^(T), a control strategy for controllingcharging power and discharging power of the system for energy storage ofthe power station for large-scale energy storage; inputting ther_(hybrid) ^(T) and the control strategy for the present time point tothe estimation module for the present time point; outputting, by theestimation module, a cost function according to the r_(hybrid) ^(T) andthe control strategy for the present time point; building, according tothe r_(hybrid) ^(T) and the cost function, an objective function fortraining the estimation module; training the estimation module accordingto the objective function to minimize the objective function; andtraining the management module according to the cost function tominimize the cost function, wherein the estimation module and themanagement module are retrained for outputting the control strategy fora next time point.